Abstract

PL-41

Microarray analysis of genome-wide gene expression patterns of tumors holds significant promise to improve the diagnosis, risk stratification, and therapy outcome prediction in cancer patients, creating exciting opportunities for development of highly anticipated individualized diagnostic, prognostic, and therapeutic applications. Here we summarize important stepping stones of recent progress in this area. Death-from-cancer signature genes. We utilized a mouse/human comparative translational genomics to identify a gene expression signature distinguishing normal self-renewing stem cells versus stem cells with diminished self-renewal function due to the loss of the BMI1 gene; this signature was then used to interrogate and interpret expression patterns of human cancers. According to the analysis of metastases and primary tumors from a transgenic mouse model of prostate cancer and cancer patients, an 11-gene BMI1-pathway signature consistently displays a normal stem cell-like expression profile in distant metastatic lesions and a subset of primary tumors. Prognostic power of an 11-gene signature was examined in several independent therapy outcome sets of clinical samples obtained from more than 2,500 cancer patients diagnosed with multiple types of cancer. Kaplan-Meier analysis demonstrates that a normal stem cell-like expression profile of the 11-gene signature in primary tumors is a consistent predictor of a short interval to disease recurrence, distant metastasis, and death after therapy in patients diagnosed with eleven distinct types of cancer. These data suggest the presence of a conserved BMI1 oncogene-driven pathway, which is activated in both normal stem cells and a highly malignant subset of human cancers diagnosed in a wide range of organs and uniformly exhibiting a marked propensity toward metastatic dissemination as well as a therapy resistance phenotype. Thus, genome-wide expression profiling studies reveal a transcriptionally discernable type of human solid tumors with a marked propensity toward metastatic dissemination, highly malignant clinical behavior, and poor therapy outcome in patients diagnosed with the early stage carcinomas of various origins. These tumors acquire full malignant potential, including an emergence and seeding of potent metastasis precursor cells, early in tumor progression. Collectively, these data suggest an involvement in development of this type of carcinomas of a highly malignant combination of oncogenic alleles conferring the proclivity to metastasize and/or targeting during malignant transformation and tumor progression of stem cells. Genetic signatures of multiple cooperating oncogenic pathways predict therapy outcome in early stage prostate and beast cancer patients. Recent expression profiling studies demonstrate the power of microarray analysis in identifying clinically relevant oncogenic pathways activated in human cancers. They provide mechanistic explanation to mounting experimental data demonstrating that there are multiple gene expression signatures predicting CTO in a given set of patients diagnosed with a particular type of cancer: presence of multiple cancer therapy outcome prediction (CTOP) models is most likely reflect deregulation of multiple oncogenic pathways, perhaps, cooperating in development of an oncogenic state. We tested this hypothesis by comparing the CTOP power of three signatures derived from corresponding transgenic mouse models of cancer and associated with activation of oncogenic pathways driven by BMI1, Myc, and Her2/neu oncogenes during the prostate and mammary carcinogenesis. Applications of three oncogenic pathway signatures outperform individual signatures in patients' stratification into statistically distinct sub-groups with distinct likelihood of therapy failure. All cancer patients with activation of three pathways (3 poor prognosis signatures) failed therapy, whereas patients with no evidence of a single pathway activation remained disease-free. These data suggest that in prostate cancer (PC) and breast cancer (BC) patients with therapy-resistant disease phenotype concomitant activation of pathways driven by BMI1, Myc, and Her2/neu oncogenes may contribute to development of highly malignant clinically lethal oncogenic state. Based on documented regulatory circuitries of several key oncogenic pathways, we propose a concept integrating cooperating oncogenic pathways, concomitant activation of which contributes to development of death-from-cancer phenotypes. Cooperating oncogenic pathways are classified in two functionally complementary categories: master oncogenic pathways and maintenance oncogenic pathways. Components of master oncogenic pathways are forming coherent linear regulatory networks with positive feed-forward and feed-back regulatory loops contributing to sustained network activation in malignant cells. Components of master oncogenic pathways are necessary for biological manifestation of several key features of a death-from-cancer therapy resistant phenotype, while components of maintenance oncogenic pathways provide regulatory signals that are essential for maintaining master oncogenic pathways in a constitutively activated state. Genetic signatures of "stemness" and self-renewal predict therapy outcome in early stage prostate and beast cancer patients. Discovery of death-from-cancer signature genes implies that genetic signatures associated with a "stemness" state such as asymmetrical division, pluripotency, and self-renewal might be informative as molecular predictors of CTO. Applying signature discovery principles to analysis of embryonic stem cells (ESC), we identified several gene expression signatures associated with a "stemness" state of ESC that appear highly information in stratification of the early-stage breast and prostate cancer patients into sub-groups with dramatically distinct likelihood of therapy failure. CTOP algorithm employing a combination of "stemness" signatures (signatures of Nanog/Sox2/Oct4-, EED-, and Suz12-patways; transposon exclusion zones (TEZ) and bivalent chromatin domains (BCD) signatures) and a Myc-driven "wound signature" demonstrates nearly 100% specificity and sensitivity of therapy outcome prediction power in retrospective analysis of large cohorts of BC and PC patients. The Rosetta stone of oncogenomics: Integration of HapMap-based SNP pattern analysis and gene expression profiling reveals common SNP profiles for CTOP genes. Recent release of a haplotype map of human genome (http://www.hapmap.org/) provides opportunity for integrative analysis on a genome scale of gene expression profiling and SNP variation patterns for discovery of genetic markers of CTO. We applied this approach for analysis of SNPs of cancer-associated genes, expression profiles of which predict the likelihood of treatment failure and death after therapy in patients with multiple types of cancer. Our analysis reveals a common SNP pattern for a majority (60 of 74; 81%) of CTOP genes, suggesting that germ-line polymorphism may have a significant impact on CTO by determining the individual's gene expression profile. Consistently, genes with SNP-driven variations in mRNA expression among normal individuals generate statistically significant CTOP models for breast, prostate, and lung cancers. Therefore, CTOP genes are distinguished by a common SNP pattern and potential utility as molecular predictors of CTO. Our analysis suggests that heritable germ-line genetic variations driven by geographically localized form of natural selection determining population differentiations may have a significant impact on CTO by influencing the individual's gene expression profile. We used this SNP pattern to identify novel gene expression models of CTO without input of expression data in the initial gene selection process. Conversely, genes with known associations with cancer incidence and severity manifest expression profiles predictive of CTO and common SNP patterns. Our analysis demonstrates that PC and BC patients with low expression of genes regulating catabolism of androgens, estrogens and thyroid hormones have a significantly higher risk of therapy failure. Interestingly, this common SNP pattern is readily discernable for genes with well-established causal role in cancer as oncogenes or tumor suppressor genes, implying the presence of cancer-related SNP profiles spanning across multiple chromosomal loci and, perhaps, generating a genome-wide cancer haplotype pattern. We tested a potential translational utility of this approach by building and retrospectively validating a CTOP algorithm integrating calls of multiple phenotype-based and SNP-based signatures of CTO. Application of a CTOP algorithm to large databases of early-stage breast and prostate tumors identifies cancer patients with 100% probability of a cure with existing therapies as well as patients with nearly 100% likelihood of treatment failure, thus providing a clinically feasible framework essential for introduction of rational evidence-based individualized therapy selection and prescription protocols. Our work demonstrates how integration of knowledge derived from multiple approaches such as comparative cross-species translational genomics, transgenic mouse models of cancer, genomics of stem cell biology, genome-wide SNP analysis, and expression profiling of human tumors would create "the Rosetta stone of oncogenomics" highlighting a roadmap to discovery of a genomics code for cancer cure.